Data-Driven Models Informed by Spatiotemporal Mobility Patterns for Understanding Infectious Disease Dynamics

Zhang, Die; Ge, Yong; Wu, Xilin; Liu, Haiyan; Zhang, Wenbin; Lai, Shengjie

doi:10.3390/ijgi12070266

Cited by 1 publication

(1 citation statement)

References 56 publications

(61 reference statements)

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“…In [7], Zhang et al proposed a machine-learning approach to predict human mobility patterns in a smart city. The authors leverage various data sources, including mobile device data, social media data, and transportation data, to train predictive models.…”

Section: Related Workmentioning

confidence: 99%

Predicting People’s Concentration and Movements in a Smart City

Ferreira,

Francisco,

Elvas

et al. 2023

Electronics

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With the rapid growth of urbanization and the proliferation of mobile phone usage, smart city initiatives have gained momentum in leveraging data-driven insights to enhance urban planning and resource allocation. This paper proposes a novel approach for predicting people’s concentration and movements within a smart city environment using mobile phone data provided by telecommunication operators. By harnessing the vast amount of anonymized and aggregated mobile phone data, we present a predictive framework that offers valuable insights into urban dynamics. The methodology involves collecting and processing location-based data obtained from telecommunication operators. Using machine learning techniques, including clustering and spatiotemporal analysis, we developed models to identify patterns in people’s movements and concentration across various city regions. Our proposed approach considers factors such as time of day, day of the week, and special events to capture the intricate dynamics of urban activities. The predictive models presented in this paper demonstrate the ability to predict areas of high concentration of people, such as commercial districts during peak hours, as well as the people flow during the time. These insights have significant implications for urban planning, traffic management, and resource allocation. Our approach respects user privacy by working with aggregated and anonymized data, ensuring compliance with privacy regulations and ethical considerations. The proposed models were evaluated using real-world mobile phone data collected from a smart city environment in Lisbon, Portugal. The experimental results demonstrate the accuracy and effectiveness of our approach in predicting people’s movements and concentration. This paper contributes to the growing field of smart city research by providing a data-driven solution for enhancing urban planning and resource allocation strategies. As cities continue to evolve, leveraging mobile phone data from telecommunication operators can lead to more efficient and sustainable urban environments.

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Section: Related Workmentioning

confidence: 99%